Method and apparatus for end-to-end travel time estimation using dynamic traffic data

ABSTRACT

The present invention is a method and an apparatus for end-to-end travel time estimation using dynamic traffic data. In one embodiment, a method for estimating a fastest route between a first point in a transportation network and a second point in the transportation network includes receiving static data relating to static characteristics of a first zone and a second zone in the transportation network. In addition, real-time data relating to real-time traffic conditions in the first zone is also received. The static and real-time data is processed in accordance with at least one fine-grained load-generation technique to generate a first load for the first zone, and the static data is generated in accordance with at least one coarse-grained load-generation technique to generate a second load for the second zone. These first and second loads are then combined, converted to travel times, and scaled to facilitate the identification of a best or fastest route.

BACKGROUND

The invention relates generally to transportation networks, and relatesmore particularly to the incorporation of dynamic data in transportationnetwork calculations.

FIG. 1 is a schematic diagram illustrating a typical large-areatransportation network 100. The transportation network 100 comprises aplurality of urban metropolitan areas 102 ₁–102 _(N) (hereinaftercollectively referred to as “metropolitan areas 102), towns 104 ₁–104_(N) (hereinafter collectively referred to as “towns 104”) andinter-urban and/or rural areas (generally designated 106) situatedbetween the metropolitan areas 102 and towns 104. The metropolitan areas102, towns 104 and inter-urban/rural areas 106 that comprise thetransportation network 100 may span a large geographical area (e.g.,comprising a plurality of cities, states, regions or countries).

When traveling between locations in a transportation network, it istypically desirable to identify a shortest path, or best (e.g., fastest)route, to travel from an origin to a destination. Conventionalapplications such as internet mapping and vehicle navigation systemstypically compute this best route based on static, non-state-dependentdata about links in the transportation network (e.g., speed limits,numbers of lanes, average loads).

A problem with this approach is that dynamic, state-dependent data thatmay influence travel time (e.g., current traffic conditions or otherenvironmental factors) is not accounted for. Thus, a computed route maynot, in fact, be the best route at a given time. Although some methodscurrently exist that do account for current traffic states, theseexisting methods are computationally intensive and limited to small ormoderately-sized geographic areas. They are thus difficult to scale tolarger, geographically heterogeneous transportation networks (such asthe transportation network 100).

Thus, there is a need for a method and an apparatus for end-to-endtravel time estimation using dynamic traffic data.

SUMMARY OF THE INVENTION

The present invention is a method and an apparatus for end-to-end traveltime estimation using dynamic traffic data. In one embodiment, a methodfor estimating a fastest route between a first point in a transportationnetwork and a second point in the transportation network includesreceiving static data relating to static characteristics of a first zoneand a second zone in the transportation network. In addition, real-timedata relating to real-time traffic conditions in the first zone is alsoreceived. The static and real-time data is processed in accordance withat least one fine-grained load-generation technique to generate a firstload for the first zone, and the static data is generated in accordancewith at least one coarse-grained load-generation technique to generate asecond load for the second zone. These first and second loads are thencombined, converted to travel times, and scaled to facilitate theidentification of a best or fastest route.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited embodiments of theinvention are attained and can be understood in detail, a moreparticular description of the invention, briefly summarized above, maybe obtained by reference to the embodiments thereof which areillustrated in the appended drawings. It is to be noted, however, thatthe appended drawings illustrate only typical embodiments of thisinvention and are therefore not to be considered limiting of its scope,for the invention may admit to other equally effective embodiments.

FIG. 1 is a schematic diagram illustrating a typical large-areatransportation network;

FIG. 2 is a flow diagram illustrating one embodiment of a method forend-to-end route prediction using state-dependent data, according to thepresent invention;

FIG. 3 is a flow diagram illustrating one embodiment of a method forgenerating travel time predictions for at least one zone of atransportation network;

FIG. 4 is a flow diagram illustrating one embodiment of a template-basedmethod for future travel time predictions;

FIG. 5 is a graph illustrating one embodiment of a template for use inaccordance with the method;

FIG. 6 is a flow diagram illustrating one embodiment of a method forestimating real-time travel times in a transportation network based onlimited real-time data;

FIG. 7 is a schematic diagram illustrating one embodiment of anexemplary transportation network including a plurality of links andnodes, as well as a park or public space; and

FIG. 8 is a high level block diagram of the present route generationsystem that is implemented using a general purpose computing device.

To facilitate understanding, identical reference numerals have beenused, where possible, to designate identical elements that are common tothe figures.

DETAILED DESCRIPTION

In one embodiment, the present invention is a method and apparatus forend-to-end travel time estimation using dynamic traffic data.Embodiments of the present invention account for real-time,state-dependent data in order to provide more accurate end-to-endestimates and predictions (e.g., shortest paths or best routes) fortransportation networks, including wide-area, spatially heterogeneoustransportation networks. Thus, embodiments of the present invention maybe implemented to advantage in applications such as internet mapping,route guidance, in-vehicle or on-board navigation, fleet routing (e.g.,for major carriers or the military) and the like.

As used herein, the terms “shortest path” or “best route” refer to oneor more individual links (e.g., road segments) in a transportationnetwork that connect a designated point of origin to a designateddestination. Specifically, a shortest path or best route represents theseries of links that, if traveled, are expected to allow one to travelfrom the origin to the destination in the least amount of time (e.g., ascompared with alternate paths or routes).

In essence, the methods and apparatuses of the present invention processa plurality of static and dynamic inputs, including link load estimates,current or real-time streaming traffic condition data (e.g., from one ormore sources including but not limited to traffic sensors, inductionloops, video feeds, cellular telephones and Global Positioning Systems(GPS)), (computed) statistical traffic patterns, real-time environmentaldata (e.g., weather conditions), radio-based real-time incident data(e.g., data pertaining to events and weather conditions, includingtraffic and accident reports), (computed) static origin-destination(O-D) matrices and static maps (e.g., digital maps), in order toidentify a best route from an origin to a destination in thetransportation network.

FIG. 2 is a flow diagram illustrating one embodiment of a method 200 forend-to-end route prediction using state-dependent data, according to thepresent invention. The method 200 may be implemented, for example, by aninternet mapping or vehicle navigation system to generate a best routebetween two transportation network endpoints (e.g., an origin and adestination) at a given time.

The method 200 is initialized at step 202 and proceeds to step 204,where the method 200 receives, e.g., from a user, a specified origin anda specified destination in the transportation network underconsideration.

The method 200 then proceeds to step 206 and generates network-wideloads (e.g., numbers of vehicles per units of time) for the entire urbanand regional zones of the transportation network, including highways(e.g., higher-density zones for which real-time traffic data feeds aretypically available). In one embodiment, these loads are generated inaccordance with a fine- or medium-grained static (non-state-dependent)or dynamic fine load-generation technique (e.g., a technique suitablefor assessing regions of fine- or medium-grained spatial dimension). Forexample, in one embodiment, the loads are generated in accordance withat least one of: static or dynamic traffic assignment, queuing networks,simulation (e.g., as typically used for modeling urban area trafficflows), probabilistic local techniques and flow propagation. In oneembodiment, the loads are generated in accordance with at least oneinput relating to the static or dynamic characteristics of zones underconsideration, such as: current or real-time traffic condition data,real-time environmental data (e.g., weather conditions), radio-basedreal-time incident data (e.g., traffic and accident reports), (computed)statistical traffic patterns, (computed) static origin-destination (O-D)matrices and static maps (e.g., digital maps).

In step 208, the method 200 generates travel time predictions for theentire inter-urban and rural zones of the transportation network underconsideration (e.g., lower-density zones for which real-time trafficdata feeds may not be available) based on the static or dynamicnetwork-wide data obtained or estimated in step 206. In one embodiment,these predictions are generated in accordance with a coarse-grainedload-generation technique (e.g., a technique suitable for assessingregions of coarse-grained spatial dimension). For example, in oneembodiment, the travel times are generated in accordance with at leastone of: template methods (e.g., as used in predicting the medium andlong-term future), statistical traffic classification, trafficassignment, simulation and probabilistic local techniques. In oneembodiment, the loads are generated in accordance with at least oneinput relating to the static or dynamic characteristics of zones underconsideration, such as: current or real-time traffic condition data,real-time environmental data (e.g., weather conditions), radio-basedreal-time incident data (e.g., traffic and accident reports), (computed)statistical traffic patterns, (computed) static origin-destination (O-D)matrices and static maps (e.g., digital maps).

The method 200 then proceeds to step 210 and obtains real-time data,where available, which is then incorporated into the current-time-stepload computations generated in steps 206 and 208. In one embodiment,real-time data is not available for all zones of the transportationnetwork. In one embodiment, the obtained real-time data includes atleast one of: current or real-time traffic condition data, real-timeenvironmental data and radio-based real-time incident data.

In step 212, the method 200 combines the generated load data for allzones in the transportation network under consideration. In oneembodiment, this combination of load data includes converting all loadsto travel times. In one embodiment, this conversion is performed inaccordance with at least one analytic model. In another embodiment, theinformation may remain as units of load (e.g., flow or density).

The method 200 then proceeds to step 214 and, where the combined loaddata has been converted to units of travel time, scales the computedtravel times in accordance with any relevant incidents or occurrences(e.g., events and weather conditions) that may affect travel timesthrough the transportation network under consideration (e.g., accidents,construction, special events or occurrences at points of interest in thetransportation network, weather and the like). In this way, moreaccurate, real-time travel times can be estimated.

The method 200 then proceeds to step 216 and identifies at least onebest route in accordance with the scaled travel times. In oneembodiment, the best route identified by the method 200 is the set oflinks (road segments) between the specified origin and specifieddestination over which travel time is expected to be the shortest (e.g.,accounting for both the static and dynamic transportation network data).

The method 200 terminates in step 218.

The method 200 is thus capable of processing a plurality of differenttypes of data relating to static and dynamic transportation networkcharacteristics in order to estimate travel times through thetransportation network. The method 200 is designed to take advantage ofreal-time, state-dependent data, where available for a given zone orlink, as well as to maximize the use of static, non state-dependent datawhen real-time data is not available. Thus, the method 200 produces amore accurate current travel time estimate than conventional routeplanning techniques. Moreover, although the method 200 has beendescribed in the context of calculating a best route for an explicitroute request (e.g., between a given origin and a given destination, asreceived in step 202), those skilled in the art will appreciate thatsteps 206–214 of the method 200 may be implemented independent of anyspecific route request, e.g., in order to maintain up-to-dateinformation about the transportation network for future route requests.

FIG. 3 is a flow diagram illustrating one embodiment of a method 300 forgenerating travel time predictions for at least one zone of atransportation network, e.g., in accordance with steps 206 and/or 208 ofthe method 200. The method 300 is initialized at step 302 and proceedsto step 304, where the method 300 computes the estimated travel timeover a given link in the zone, e.g., in accordance with observed(current) or predicted (future) traffic patterns over the link. Thisestimation may be computed in accordance with any of the methodsdescribed above, or in accordance with a template-based statisticalmethod described in further detail with respect to FIG. 4.

Once the estimated travel time has been computed for the link, themethod 300 proceeds to step 306 and updates a total travel time estimatefor at least one route including the link. The method 300 then returnsto step 304 and proceeds as described above, this time computing theestimated travel time over a second link in the transportation network.This iterative process is repeated on a link-by-link basis to obtain atotal estimated travel time for a route comprising one or more links.

FIG. 4 is a flow diagram illustrating one embodiment of a template-basedmethod 400 for future travel time predictions, e.g., for use inaccordance with the method 300 (and therefore steps 206 and/or 208 ofthe method 200). Specifically, in one embodiment, the method 400identifies, on a link-by-link basis, the traffic state characteristics(e.g., speed, volume, etc.) that best characterize the progression ofthat traffic state into the future. Predictions of future travel timesover a given link are then made in accordance with the observed trafficstate (e.g., “peak weekday traffic volumes typically occur between 8:00AM and 9:00 AM”). Although the method 400 will be described in thecontext of predicting travel times for inter-urban and rural zones,those skilled in the art will appreciate that the method 400 may alsoused to predict travel times for urban zones as well.

The method 400 is initialized at step 402 and proceeds to step 404,where the method 400 initializes a template that will reflect arepeating behavior of a traffic pattern on a given link of thetransportation network. In one embodiment, the template maps trafficvolume (e.g., numbers of vehicles per unit of time) over a given linkversus time, in order to illustrate a traffic pattern. This pattern mayrepresent daily, weekly, monthly, or yearly behavior, or may be tailoredover any other useful time horizon. For example, a template representinga daily traffic pattern for a link could comprise twenty-four datapoints (one for each hour of the day) t(0), . . . , t(23) such thatt(0)+ . . . + t(23)=1. In one embodiment, t(0) represents midnight of agiven day. From this information, an estimate of travel time over thelink at a given time in the future can be derived.

Moreover, templates representing multiple time horizons may bemaintained for a single link. In one embodiment, the templateinitialized in step 402 is associated with an initialized traffic volumeof zero, e.g., the initialized template contains no data. Thus, in theexample of a daily template above, the template is initialized such thatt(0), . . . , t(23)=1/24, v(0)=0 and i=0, where v(i) is the estimate, attime t(i), of the total traffic volume over twenty-four hours, based oninformation up to time t(i-1).

In step 406, the method 400 receives a data point for incorporation inthe template. The data point represents, for example, real-time trafficvolume at a given time on the link for which the template is generated.In one embodiment, the data point is a point in an incoming data stream(e.g., where a new data point is received every hour). For example, thedata stream could represent the number of vehicles, x, passing by aparticular marking point on the link, such that a received data pointsrepresents x(i), or the number of vehicles passing the marking point attime t(i).

The method 400 then proceeds to step 408 and updates the current volumeestimate for the given time in accordance with the received data point.In one embodiment, the current volume estimate is updated in accordancewith a moving average (e.g., an exponentially weighted moving average)that smoothes out jitters in received data and captures gradual datashifts. For example, following the exemplary embodiment of the dailytemplate above, an update of the current volume estimate in accordancewith step 408 could involve setting

${v\left( {i + 1} \right)} = {{\left( {1 - \alpha} \right){v(i)}} + \frac{\alpha\;{x(i)}}{t\left( {i\mspace{11mu}{\% 24}} \right)}}$where α is afree variable representing the level of sensitivity of the method 400and has a value between zero and one. In one embodiment, α has a valuebetween 0.4 and 07. The larger the value of α, the less sensitive andthe more adaptive to changing traffic patterns the method 400 is.

In step 410, the method 400 generates a prediction p(i, j) for thenumber of vehicles that are expected to pass the marking point at afuture time t(i+j), in accordance with the updated volume estimate. Inone embodiment, the prediction p(i, j) is generated such that p(i,j)=v(i+1)t((i+j)%24). Thus, the prediction p(i, j) is based on observedinformation (e.g., traffic volumes) up to time t(i), at which theprediction p(i, j) is generated.

The method 400 then proceeds to step 412 and updates the currenttemplate estimate in accordance with the generated predictions p(i, j)(i.e., computed statistical traffic patterns) and one or more maps ofthe transportation network including the link. The template estimate isan estimate of the traffic pattern over time (such as an increasingpattern in the morning hours and a decreasing pattern after the eveningrush hours). The template t(i) is normalized, as discussed above, sothat the sum of t(0)+t(1)+ . . . +t(24) is equal to one. In oneembodiment, the current template estimate is updated in accordance witha moving average (e.g., an exponentially weighted moving average) thatsmoothes out jitters in received data and captures gradual data shifts.For example, following the exemplary embodiment of the daily templateabove, an update of the current template estimate in accordance withstep 412 could involve setting

${t(i)} = {{\left( {1 - \beta} \right){t(i)}} + \frac{\beta\;{x\left( {i\mspace{11mu}{\% 24}} \right)}}{{x(i)} + {x\left( {i - 1} \right)} + \ldots + {x\left( {i - 23} \right)}}}$where β, like α, is a free variable representing the level ofsensitivity of the method 400 and has a value between zero and one. Inone embodiment, β has a value between 0.4 and 07. The larger the valueof β, the less sensitive and the more adaptive to changing trafficpatterns the method 400 is.

The method 400 then returns to step 406, where the method 400 receives anew data point (e.g., where i=i+1) and proceeds as described above inorder to adapt the template to ongoing traffic volumes on the link.Thus, the generated travel time estimates are a function of both thecomposition of the template and the ongoing traffic volume. The method400 may be repeated link-by-link for each link in the transportationnetwork.

The method 400 therefore learns from past observed traffic patterns andis refined over time using real-time data (e.g., without user input).The method 400 is thus capable of quickly catching up with shifts intrends or traffic patterns. This is especially significant, for example,where an overall traffic volume may shift up or increase in relation toa general observed pattern for a particular day. The method 400 canquickly detect this increase in volume as it develops and adjustpredictions for future periods accordingly. Thus, by making use ofobserved, time-dependent state data in future predictions (as opposed tousing average values), more accurate predictions can be generated. Insome embodiments, the method 400 is particularly effective in predictingshort-, medium- and long-term future conditions.

FIG. 5 is a graph illustrating one embodiment of a template 500 for usein accordance with the method 400. As described above, in oneembodiment, the template 500 maps traffic volume (e.g., numbers of carsper unit of time) over a given link versus time (e.g., approximatelythree days in the case of FIG. 5). Thus, the template may be marked bypeaks 502 ₁–502 _(n) (hereinafter collectively referred to as “peaks502”) where the traffic volume is greatest (e.g., such as during morningor afternoon rush hours), plateaus 504 ₁–504 _(n) (hereinaftercollectively referred to as “plateaus 504”) where traffic volume remainsrelatively constant (e.g., between morning and afternoon rush hours),and valleys 506 ₁–506 _(n) (hereinafter collectively referred to as“valleys 506”) where traffic volume is lightest.

As described above, the template 500 thus enables the prediction offuture traffic patterns or volumes over a link at a given time based onhistorical traffic volumes over the same link. Real-time data may beincorporated in the template 500 as the data is received, in order toquickly identify traffic patterns that may deviate from the historicalnorm and to predict the effects of these changing traffic patterns intothe future.

FIG. 6 is a flow diagram illustrating one embodiment of a method 600 forestimating real-time travel times in a transportation network based onlimited real-time data, e.g., for use in accordance with steps 206and/or 208 of the method 200. Specifically, in one embodiment, themethod 600 estimates travel times over links in the transportationnetwork for which link-specific real-time data is not available.Although the method 600 will be described in the context of predictingtravel times for urban zones, those skilled in the art will appreciatethat the method 600 may also used to predict travel times forinter-urban and rural zones as well.

The method 600 is initialized at step 602 and proceeds to step 604,where the method 600 receives one or more static or dynamic parametersof the transportation network (e.g., links, nodes or intersections oftwo or more links, free-flow speeds and likely origins and destinationssuch as parking garages, on-street parking spots and other points ofinterest).

The method 600 then proceeds to step 606 and obtains at least one set oflink flows over the entire transportation network. In one embodiment,more than one set of link flows for the entire transportation networkmay be obtained, such as one set of link flows for peak periods and oneset of link flows for off-peak periods, or one set of link flows for aweekday and one set of link flows for weekends, or separate sets of linkflows for different time periods over a typical day or week.

In one embodiment, the link flows may be derived from at least oneorigin-destination (O-D) trip table (e.g., via trip assignment). In oneembodiment, these O-D trip tables are static tables for an average timeperiod on an average day and can be obtained, for example, from theassociated metropolitan planning organization. In another embodiment,the O-D trip tables may be time-dependent, or may represent peak oroff-peak times, weekday versus weekend, or may be hourly, etc. If thelink flows are to be derived from one or more O-D trip tables, then themethod 600 computes a traffic assignment for each O-D trip table. In oneembodiment, this traffic assignment is computed in accordance with aone-period traffic assignment method, such as a known traffic assignmentmethod. From each traffic assignment, the method 600 can then obtainlink flows for all links in the transportation network for the averagetime period in which the O-D trip table is valid.

In step 608, the method 600 uses the link flows to determine thesplitting probabilities at each node in the transportation network, fromeach incoming link. A splitting probability refers to the percentages ofvehicles that go left, right and straight through a given node orintersection. For example, the average case for a given node may dictatethat sixty percent of traffic arriving at the node goes straight, thirtypercent of the traffic turns right, and ten percent of the traffic turnsleft. This information is computed and stored for each node in thetransportation data, in accordance with the node's average case data(e.g., as obtained from one or more sets of link flows or one or moreO-D trip tables).

The method 600 then proceeds to step 610 and receives a real-time datafeed associated with a given link in the transportation network (e.g.,relating to current traffic volume, flow or speed over the link). Thisreal-time data feed may be received from, for example, a sensor (e.g., amotion sensor, a camera or other real-time data collection mechanism)placed on the link. In one embodiment, such real-time data feeds areavailable only for a limited number of links in the transportationnetwork. If the real-time data feed is received in speed units, themethod 600 converts the value to flow units.

In step 612, the method 600 applies the real-time flows to the computedsplitting percentages for each node and propagates the real-time flowsthroughout the transportation network, in order to estimate thereal-time volumes on the links of the transportation network. In oneembodiment, the real-time flows are applied to the computed splittingprobabilities in accordance with one or more flow balance equations. Inone embodiment, this is done using a set of network flows that closelyresembles the current time period.

The method 600 then proceeds to step 614 and applies one or more specialtechniques to account for absorption (e.g., for different types ofparking garages, parking meters, points of interest, etc. in thetransportation network that may absorb some of the traffic flow oncertain links). In one embodiment, absorption is accounted for bydeducting a fixed or variable percentage or absolute quantity of load(e.g., flow or density) from the link load at one or more relevant timeperiods, where the quantity deducted depends on the nature of theattraction points on the link (e.g., parking garages, points ofinterest, etc.) and the time of day, day of week, etc. For example, loadis typically absorbed from a link load when vehicles enter a parkinggarage on the link. In an analogous manner, traffic generation may beaccounted for on those or other links by augmenting the load (e.g., flowor density) on the link in accordance with the time of day, day of week,etc. and the nature of attraction points on the link. For example, loadis typically augmented on a link when vehicles exit a parking garage onthe link at the end of the work day. In one embodiment, absorption andorigin states are changeable over time, for example as parking ruleschange during the day.

In optional step 616 (illustrated in phantom), the method 600 determineswhether inconsistent flow is exhibited on any link in the transportationnetwork and, if so, generates a mega-node (e.g., an artificial noderepresenting—and merging or combining the characteristics of—two or morenetwork nodes, and suppressing the links between those network nodes)incorporating that link. This mega-node is generated dynamically usingonly observed, real-time flows. The method 600 then determines theupdated splitting probabilities for the mega-node and applies thesesplitting probabilities to the inconsistent link flow.

The method 600 then returns to step 608 and proceeds as described abovefor a next node in the transportation network.

FIG. 7 is a schematic diagram illustrating one embodiment of anexemplary transportation network 700 including a plurality of links 702and nodes 704, as well as a park or public space 706. As furtherillustrated, some, but not all, of the links 702 are associated withsensors 708 (illustrated as darkened links 702) that provide real-timedata feed of current traffic volume, flow or speed over the associatedlink 702.

As illustrated, a sensor 708 placed along the link 702′ may observe areal-time traffic flow over the link 702′ of approximately 15 vehiclesper second. Moreover, the splitting probabilities for a node 704′including the link 702′ may be computed such that twenty percent of thetraffic flow from the link 702′ is expected to go straight through thenode 704′ and forty percent of the traffic flow from the link 702′ isexpected to turn left at the node 704′.

FIG. 8 is a high level block diagram of the present route generationsystem that is implemented using a general purpose computing device 800.In one embodiment, a general purpose computing device 800 comprises aprocessor 802, a memory 804, a route generator or module 805 and variousinput/output (I/O) devices 806 such as a display, a keyboard, a mouse, amodem, and the like. In one embodiment, at least one I/O device is astorage device (e.g., a disk drive, an optical disk drive, a floppy diskdrive). It should be understood that the route generator 805 can beimplemented as a physical device or subsystem that is coupled to aprocessor through a communication channel.

Alternatively, the route generator 805 can be represented by one or moresoftware applications (or even a combination of software and hardware,e.g., using Application Specific Integrated Circuits (ASIC)), where thesoftware is loaded from a storage medium (e.g., I/O devices 806) andoperated by the processor 802 in the memory 804 of the general purposecomputing device 800. Thus, in one embodiment, the route generator 805for generating a best route from an origin to a destination in atransportation network described herein with reference to the precedingFigures can be stored on a computer readable medium or carrier (e.g.,RAM, magnetic or optical drive or diskette, and the like).

Thus, the present invention represents a significant advancement in thefield of travel time estimation for transportation networks. Embodimentsof the present invention may be implemented to account for real-time,state-dependent transportation network data in order to provide moreaccurate end-to-end estimates and predictions (e.g., shortest paths orbest routes) for travel times over the transportation network, evenwhere the transportation network is a wide-area, spatially heterogeneoustransportation network. Moreover, a plurality of techniques may beimplemented to estimate current and future traffic conditions fordifferent zones of the transportation network, where different types ofdata may be available for different zones. Thus, embodiments of thepresent invention may be implemented to advantage in applications suchas internet mapping, route guidance, in-vehicle or on-board navigation,fleet routing (e.g., for major carriers or the military) and the like.

While foregoing is directed to the preferred embodiment of the presentinvention, other and further embodiments of the invention may be devisedwithout departing from the basic scope thereof, and the scope thereof isdetermined by the claims that follow.

1. A method for estimating a fastest route between a first point in atransportation network and a second point in the transportation network,the method comprising: receiving static data relating to staticcharacteristics of a first zone and at least a second zone in saidtransportation network; receiving real-time data relating to real-timetraffic conditions in said first zone; processing said static data andsaid real-time data in accordance with at least one fine-grainedload-generation technique to generate a first load for said first zone,said first load comprising a number of vehicles traveling in said firstzone per a unit of time; and processing said static data in accordancewith at least one coarse-grained load-generation technique to generate asecond load for said second zone, said second load comprising a numberof vehicles traveling in said second zone per a unit of time, whereinsaid fastest route is estimated based on said first load and said secondload.
 2. The method of claim 1, wherein said static data comprises atleast one of: link load estimates, statistical traffic patterns, staticorigin-destination trip tables and static maps.
 3. The method of claim1, wherein said real-time data comprises at least one of: currenttraffic condition data, real-time traffic condition data, environmentaldata and radio-based incident data.
 4. The method of claim 1, whereinsaid at least one fine-grained load-generation technique is at least oneof: dynamic traffic assignment, a queuing network, simulation, aprobabilistic local technique and flow propagation.
 5. The method ofclaim 1, wherein said at least one coarse-grained load-generationtechnique is at least one of: a template method, statistical trafficclassification, traffic assignment, simulation and a probabilistic localtechnique.
 6. The method of claim 1, wherein said first zone is an urbanzone or a regional zone.
 7. The method of claim 1, wherein said secondzone is a rural zone or an inter-urban zone.
 8. The method of claim 1,further comprising: combining said first load and said second load togenerate network-wide loads.
 9. The method of claim 8, wherein saidcombining further comprises: converting said network-wide loads tocorresponding estimated travel times.
 10. The method of claim 9, furthercomprising: scaling said estimated travel times in accordance with atleast one relevant incident in said transportation network.
 11. Themethod of claim 10, wherein said at least one relevant incident is atleast one of: a traffic accident in said transportation network,occurrence at a point of interest in said transportation network,construction occurring in said transportation network and a weathercondition in said transportation network.
 12. The method of claim 10,further comprising: identifying said fastest route in accordance withsaid scaled travel times.
 13. A computer readable medium containing anexecutable program for estimating a fastest route between a first pointin a transportation network and a second point in the transportationnetwork, where the program performs the steps of: receiving static datarelating to static characteristics of a first zone and at least a secondzone in said transportation network; receiving real-time data relatingto real-time traffic conditions in said first zone; processing saidstatic data and said real-time data in accordance with at least onefine-grained load-generation technique to generate a first load for saidfirst zone, said first load comprising a number of vehicles traveling insaid first zone per a unit of time; and processing said static data inaccordance with at least one coarse-grained load-generation technique togenerate a second load for said second zone, said second load comprisinga number of vehicles traveling in said second zone per a unit of time,wherein said fastest route is estimated based on said first load andsaid second load.
 14. The computer readable medium of claim 13, whereinsaid static data comprises at least one of: link load estimates,statistical traffic patterns, static origin-destination trip tables andstatic maps.
 15. The computer readable medium of claim 13, herein saidreal-time data comprises at least one of: current traffic conditiondata, real-time traffic condition data, environmental data andradio-based incident data.
 16. The computer readable medium of claim 13,wherein said at least one fine-grained load-generation technique is atleast one of: dynamic traffic assignment, a queuing network, simulation,a probabilistic local technique and flow propagation.
 17. The computerreadable medium of claim 13, wherein said at least one coarse-grainedload-generation technique is at least one of: a template method,statistical traffic classification, traffic assignment, simulation and aprobabilistic local technique.
 18. The computer readable medium of claim13, further comprising: combining said first load and said second loadto generate network-wide loads; converting said network-wide loads tocorresponding estimated travel times; and scaling said estimated traveltimes in accordance with at least one relevant incident in saidtransportation network.
 19. The computer readable medium of claim 18,further comprising: identifying said fastest route in accordance withsaid scaled travel times.
 20. Apparatus for estimating a fastest routebetween a first point in a transportation network and a second point inthe transportation network, the apparatus comprising: means forreceiving static data relating to static characteristics of a first zoneand at least a second zone in said transportation network; means forreceiving real-time data relating to real-time traffic conditions insaid first zone; means for processing said static data and saidreal-time data in accordance with at least one fine-grainedload-generation technique to generate a first load for said first zone,said first load comprising a number of vehicles traveling in said firstzone per a unit of time; and means for processing said static data inaccordance with at least one coarse-grained load-generation technique togenerate a second load for said second zone, said second load comprisinga number of vehicles traveling in said second zone per a unit of time,wherein said fastest route is estimated based on said first load andsaid second load.